Brandwatch MCP Server for LlamaIndex 8 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Brandwatch as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Brandwatch. "
"You have 8 tools available."
),
)
response = await agent.run(
"What tools are available in Brandwatch?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Brandwatch MCP Server
Connect your Brandwatch Consumer Research account to any AI agent and orchestrate your social listening and data analysis workflows through natural conversation.
LlamaIndex agents combine Brandwatch tool responses with indexed documents for comprehensive, grounded answers. Connect 8 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Project & Dashboard Navigation — List and retrieve detailed metadata for all your active research projects and dashboards.
- Query Management — Access your configured search queries to monitor brand health and industry trends.
- Mention Retrieval — Query and inspect raw social mentions based on specific queries and date ranges.
- Data Aggregation — Retrieve volume aggregates to analyze mention trends and spikes over time.
- Tag Coordination — List and create categorization tags to organize your social data effectively.
The Brandwatch MCP Server exposes 8 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Brandwatch to LlamaIndex via MCP
Follow these steps to integrate the Brandwatch MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 8 tools from Brandwatch
Why Use LlamaIndex with the Brandwatch MCP Server
LlamaIndex provides unique advantages when paired with Brandwatch through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Brandwatch tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Brandwatch tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Brandwatch, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Brandwatch tools were called, what data was returned, and how it influenced the final answer
Brandwatch + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Brandwatch MCP Server delivers measurable value.
Hybrid search: combine Brandwatch real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Brandwatch to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Brandwatch for fresh data
Analytical workflows: chain Brandwatch queries with LlamaIndex's data connectors to build multi-source analytical reports
Brandwatch MCP Tools for LlamaIndex (8)
These 8 tools become available when you connect Brandwatch to LlamaIndex via MCP:
create_tag
Create a new tag for categorizing mentions
get_mentions
Retrieve mentions for a specific query
get_project
Get details of a specific project
get_volume_aggregates
Get mention volume aggregates for a query
list_dashboards
List dashboards in a project
list_projects
List all active projects
list_queries
List configured queries in a project
list_tags
List tags available in a project
Example Prompts for Brandwatch in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Brandwatch immediately.
"List all queries configured in project proj_1."
"Get volume aggregates for query q_1 from Jan 1st to Jan 31st."
"Create a new tag called 'Urgent Review' in project proj_1."
Troubleshooting Brandwatch MCP Server with LlamaIndex
Common issues when connecting Brandwatch to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpBrandwatch + LlamaIndex FAQ
Common questions about integrating Brandwatch MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Brandwatch with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Brandwatch to LlamaIndex
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.